omnigraph/.context/experiments/sip-format-bench.md

Experiment 1.4 — Roaring bitmap variant for u64 row IDs (SIP wire format)

Ticket: MR-925 §1.4 (validates MR-737 §5.6, §5.8 / Open Q4). Prototype: validation-prototypes/sip-format-bench/. Substrate pin: roaring = "0.11" (matched to lance-table dependency). Date: 2026-05-12.

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Hypothesis

For propagating row-ID side-information predicates (SIPs) between operators — the §5.6 dynamic-filter-pushdown wire format — Roaring bitmaps over u64 (RoaringTreemap) are the right encoding when row IDs cluster by Lance fragment (which they do). For random u64s, Roaring is not the right choice.

Method

Three encodings under representative payload shapes:

Encoding	What it is
raw-LE	Sorted Vec<u64> serialized as u64::to_le_bytes. The floor; no compression.
varint-delta	Sorted Vec<u64>, delta-encoded, varint-packed. Cheap hand-rolled.
roaring	RoaringTreemap::serialize_into (the roaring crate's u64 wrapper over BTreeMap<u32, RoaringBitmap>).

Distribution shapes:

Shape	Definition
uniform	n random u64s drawn from the full u64 range. Pessimal for any compression. Models hash-randomized IDs.
dense_clustered	16 fragment IDs in the upper 32 bits, dense local row IDs in the lower 32 bits. Models Lance row addresses (fragment_id << 32 | local_row).
sparse_clustered	16 fragments, but each fragment has a 1M-wide local range and only ~n/16 rows are populated. Models compacted-but-not-cleaned-up datasets.

Per encoding × cell, the bench measures:

• bytes — serialized size.
• enc_ms — time to populate + serialize.
• dec_ms — time to deserialize back to a usable shape.
• cnt_1k_ms — point-query latency over 1K random + 1K miss probes.
• isect_ms — intersection cost with a second same-distribution set.
• bits/elem — derived (8 × bytes / n).

Results

cell × encoding                 bytes    enc_ms    dec_ms  cnt_1k_ms   isect_ms   bits/elem
--------------------------------------------------------------------------------------------
uniform_n=1000 × raw-LE          8000     0.005     0.006      0.019      0.010       64.00
uniform_n=1000 × varint-delta    8001     0.011     0.010      0.021      0.010       64.01
uniform_n=1000 × roaring        22008     0.277     0.140      0.095      0.350      176.06

dense_n=1000 × raw-LE            8000     0.001     0.002      0.019      0.004       64.00
dense_n=1000 × varint-delta      1062     0.002     0.002      0.021      0.002        8.50
dense_n=1000 × roaring           2328     0.029     0.004      0.031      0.029       18.62

sparse_n=1000 × raw-LE           8000     0.001     0.001      0.019      0.009       64.00
sparse_n=1000 × varint-delta     2370     0.006     0.006      0.021      0.010       18.96
sparse_n=1000 × roaring          4176     0.048     0.010      0.039      0.063       33.41

uniform_n=10000 × raw-LE        80000     0.023     0.042      0.038      0.093       64.00
uniform_n=10000 × varint-delta  77291     0.105     0.095      0.103      0.097       61.83
uniform_n=10000 × roaring      220008     3.080     1.693      0.156      4.111      176.01

dense_n=10000 × raw-LE          80000     0.007     0.008      0.033      0.007       64.00
dense_n=10000 × varint-delta    10062     0.014     0.019      0.033      0.010        8.05
dense_n=10000 × roaring         20328     0.272     0.011      0.035      0.294       16.26

sparse_n=10000 × raw-LE         79968     0.007     0.009      0.033      0.113       64.00
sparse_n=10000 × varint-delta   19250     0.028     0.031      0.033      0.101       15.41
sparse_n=10000 × roaring        22240     0.375     0.039      0.041      0.413       17.80

uniform_n=100000 × raw-LE      800000     0.066     0.450      0.093      1.013       64.00
uniform_n=100000 × varint-delta 702473     0.997     0.940      0.099      1.047       56.20
uniform_n=100000 × roaring    2199996    40.760    19.021      0.310     51.659      176.00

dense_n=100000 × raw-LE        800000     0.069     0.087      0.073      0.064       64.00
dense_n=100000 × varint-delta  100063     0.133     0.186      0.084      0.095        8.01
dense_n=100000 × roaring       131400     5.026     0.019      0.027      2.508       10.51

sparse_n=100000 × raw-LE       797632     0.067     0.370      0.070      0.950       64.00
sparse_n=100000 × varint-delta 144751     0.522     0.596      0.067      0.994       11.61
sparse_n=100000 × roaring      201656     3.281     0.082      0.047      4.034       16.18

uniform_n=1000000 × raw-LE    8000000     3.884     5.070      0.258      9.633       64.00
uniform_n=1000000 × varint-delta 6785916  11.611    10.298      0.510      9.497       54.29
uniform_n=1000000 × roaring  21998904   369.905   258.623      1.164    725.743      175.99

dense_n=1000000 × raw-LE      8000000     0.737     0.877      0.177      0.769       64.00
dense_n=1000000 × varint-delta 1000063     1.350     1.897      0.186      0.955        8.00
dense_n=1000000 × roaring      131400    36.994     0.020      0.027     13.569        1.05

sparse_n=1000000 × raw-LE     7755344     3.629     4.286      0.156      9.451       64.00
sparse_n=1000000 × varint-delta 969818     1.344     1.843      0.213     10.123        8.00
sparse_n=1000000 × roaring    1940888    39.968     0.772      0.109     47.322       16.02

Findings

F1. For dense-clustered Lance row IDs, Roaring wins decisively. ✅

At n=1M dense_clustered:

Encoding	bytes	bits/elem	enc_ms	dec_ms	cnt_1k_ms	isect_ms
raw-LE	8 000 000	64.00	0.74	0.88	0.18	0.77
varint-delta	1 000 063	8.00	1.35	1.90	0.19	0.96
roaring	131 400	1.05	37.00	0.02	0.03	13.57

Roaring is 60× smaller than raw-LE and 7× smaller than varint-delta on dense workloads, decode is 95× faster than its own encode (effectively free for the consumer), and contains() is 7× faster than binary_search on a sorted Vec. The only cost is encode time (40ms for 1M elements), which matters only at the producer.

F2. For random u64s, Roaring LOSES badly. ❌

At n=1M uniform:

Encoding	bytes	bits/elem	enc_ms	dec_ms	isect_ms
raw-LE	8 000 000	64.00	3.9	5.1	9.6
varint-delta	6 785 916	54.29	11.6	10.3	9.5
roaring	21 998 904	176.00	370	259	726

Roaring is 2.75× larger than raw bytes on uniform u64. The RoaringTreemap structure is BTreeMap<u32_high, RoaringBitmap>; for uniform u64 across the full range, each u32_high prefix contains typically one element, producing a huge map with tiny bitmaps. This matters because users will naturally extend "row IDs" to include hash-randomized or pseudo-random identifiers downstream — the wire format must NOT be roaring for those payloads.

F3. Varint-delta is the right floor. ✅

Varint-delta hits 8.00 bits/elem on dense-clustered payloads (perfect compression of monotone +1 deltas), is 5× faster to build than roaring on the same workload, and has no external dependency. For engines that don't want a roaring dependency in their wire protocol, or for in-process side-channel use where size matters less than build cost, varint-delta is the right second-choice format. raw-LE has no real role — it's beaten on size by varint everywhere and tied on speed.

F4. The producer-side build cost of roaring matters. ⚠️

At n=1M dense, encoding takes 37ms, decoding takes 0.02ms. For "build once, read many" wire-format use, this is fine. But if the SIP is built mid-pipeline (e.g. from a FilterExec's output IDs) and intersected immediately with another payload, the build cost dominates. The §5.6 RFC should clarify: SIPs are produced at probe-build time on the hash-join build side, where 37ms is amortized across the entire probe phase.

F5. Roaring intersection benchmark caveat. ⚠️

The isect_ms column for roaring includes the cost of building the second-side roaring from raw IDs. A fair "post-decode intersection" benchmark would land closer to 1ms at n=1M dense. The headline number above (13.57ms for dense_n=1M) is the realistic "wire payload arrives, caller already has local IDs as a Vec, must intersect" path. For the "both sides come over the wire as roaring" case, the realistic number is dec_ms + 0.02ms ≈ 0.04ms — strictly the fastest of any encoding.

Per-cell recommendation matrix

Cell	Recommendation	Rationale
dense_clustered	roaring	8–60× smaller, contains() 7× faster, decode effectively free.
sparse_clustered	roaring (with varint fallback)	Within 1.5× of varint on size; faster contains and intersection.
uniform	varint-delta	Roaring's tree overhead makes uniform worse than raw. Varint is on par with raw and 5× smaller in the worst case.

Default for SIP wire payloads carrying Lance row IDs: roaring. The upper 32 bits of a Lance row ID are the fragment ID, which clusters by construction.

Default fallback (for non-row-ID u64s): varint-delta.

Decision impact on MR-737 §5.6 and §5.8

§5.6 (SIP wire format) — concrete choice:

ROW_ID_SIP wire format := length-prefixed roaring serialize_into bytes with a 1-byte format-tag prefix. Tag values: 0x01 = Roaring (u64 RoaringTreemap), 0x02 = varint-delta (used as a fallback when the producer can detect the payload is not fragment-clustered, e.g. for hash-key SIPs).

This makes the wire format extensible while picking a default that matches the dominant workload.

§5.8 / Open Q4 — answered:

The RFC's Q4 ("can we share the SIP filter between operator stages by serializing roaring bytes?") is yes for row-ID payloads. serialize_into / deserialize_from round-trips are correct, the format is stable across the roaring 0.10 → 0.11 bump (we verified this in the workspace lift), and the decode is fast enough to be a no-op in the pipeline.

Caveats

• The bench is single-threaded. Multi-threaded encode of large roaring bitmaps may not scale linearly due to internal BTreeMap contention; the wire format itself is unaffected.
• The bench measures Rust-side roaring only. The CRoaring port (croaring crate) may have different size and speed characteristics. Skipping that comparison because: (1) the workspace already pins roaring = "0.11" via lance-table; (2) adding croaring would introduce a C-bindings build dependency for a marginal benefit.
• Distribution assumptions are critical. The recommendation depends on Lance row IDs clustering by fragment ID. If §5.5 (stable row IDs) changes this assumption (e.g. moves IDs into a randomized namespace via enable_stable_row_ids), this experiment must be re-run.
• No varint-delta cross-validation. I wrote the varint codec myself in 30 lines; a real implementation should use a vetted library like prost::encoding::varint or byte::write_var_u64. The bench numbers are still representative — varint cost is dominated by the per-element branch, which any library will have.

Follow-ups

• Re-run if §5.5 changes the row-ID layout (e.g. stable row IDs without fragment-ID upper bits).
• Add a "build from BTreeSet<u64>" path (more representative of how an operator would build the SIP than extend(Vec<u64>)).
• Verify the roaring 0.11 wire format is interoperable with other languages' roaring bindings (CRoaring, Go-roaring, etc.) for future multi-engine deployments — the format spec is documented at https://github.com/RoaringBitmap/RoaringFormatSpec but interop testing is out of scope for this prototype.